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Record W4390674101 · doi:10.1680/jenes.23.00071

Green hydrogen production using proton membrane electrolysis of clean drinking water

2024· article· en· W4390674101 on OpenAlexvenueno aff
Jasmin Søgaard-Deakin, George Xydis

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2024
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
Fundersnot available
KeywordsHydrogen productionElectrolysisEnvironmental scienceElectrolysis of waterHydrogenChemistryProduction (economics)Environmental chemistryPulp and paper industryEnvironmental engineeringWaste managementElectrodeEngineeringElectrolyte

Abstract

fetched live from OpenAlex

With the EU’s green strategy and its member countries making a conscious effort to move away from fossil fuels and cut greenhouse gas emissions, there has been an uptick in technologies that produce green energy. Power-to-X has become an important topic in recent times, due to the so-called emission-free process of creating hydrogen from excess renewable energy. This research investigated this claim of being emission-free by analysing the method of obtaining extremely clean (pure) drinking water to produce hydrogen. A linear equation was produced that determines the carbon dioxide (CO 2 ) intensity per kilowatt-hour of electricity used in producing a kilogram of water used to produce hydrogen. It was found that carbon dioxide emissions were produced by small-, medium- and large-capacity projects based in Denmark. Indicatively, for a 1 GW project facility, it was calculated that such a plant can produce 160 161 kg of carbon dioxide per year, so apparently, nothing comes without a cost.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.187
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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